When it comes to product innovation, starting off in the wrong direction can be tragic for a brand—and expensive. So when it comes time to get started, it’s no surprise that companies looking to innovate often first turn to their consumer research departments for key insights and direction.

But with sluggish sales growth across the consumer goods landscape, brands know that they have to innovate better—and make every dollar count. And as more C-level executives scrutinize their consumer research investments—knowing that less than 1% of new products deliver $50 million in revenue in their first year—they’re asking some key questions about what they should expect to get back in return. The bottom line in all of those questions is simple: What is the impact of consumer research on innovation?

To find out, Nielsen recently tested the effectiveness of evolutionary optimization, a common method for evaluating new product ideas. For the studies, which sought to understand the method’s impact on revenue, Nielsen put 20 randomly selected new product initiatives to the test. The results found that, on average, evolutionary optimization identified concepts that yielded 38% more in forecasted revenue than concepts selected using other methods. That percentage represented $13 million more for the brand.

In other words, brands that do not use evolutionary algorithms to optimize their concepts sacrifice roughly one-third of their potential revenue.

HOW DOES EVOLUTIONARY OPTIMIZATION WORK?

Traditionally, marketers measure the strength of their new product ideas by constructing a concept, which sets up a consumer problem and then communicates how the product uniquely solves it. When companies employ evolutionary optimization, they set up cross-functional teams that collaborate and identify a wide range of ideas about a product, including different consumer insights, features, claims, and taglines. Then they enter their ideas into software that makes it easy to organize and review them.

From there, the software tests hundreds to millions of concept alternatives—different combinations of the ideas generated in the initial phase—with consumers. Consumers view different concept alternatives online and select the options they prefer most. In the process, the algorithm learns from their choices, evolving the most favorable combination of elements.

Assuming a cost of approximately $50,000 per optimization study, Nielsen identified that the average return on investment for evolutionary optimization was 259X. In other words, for every dollar spent on optimization, innovation teams earned back an average of $259 for the business.